AIMC Topic: Osteoporosis

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Enhancing osteoporosis risk prediction using machine learning: A holistic approach integrating biomarkers and clinical data.

Computers in biology and medicine
Osteoporosis (OP) affects approximately 18 % of the global population, with osteoporosis-associated fractures impacting up to 37 million people annually. While dual-energy X-ray absorptiometry (DXA) remains the gold standard for diagnosis, its limita...

Application of machine learning algorithms in osteoporosis analysis based on cardiovascular health assessed by life's essential 8: a cross-sectional study.

Journal of health, population, and nutrition
BACKGROUND: Life's Essential 8 (LE8) for assessing cardiovascular health (CVH) has been demonstrated to be inversely associated with osteoporosis (OP). This study aims to create a machine learning (ML) model to assess the clinical association value o...

Comparative Analysis of Feature Extraction Methods and Machine Learning Models for Predicting Osteoporosis Prevalence.

Journal of medical systems
This study systematically examined the impact of three feature selection techniques (Boruta, Extreme gradient boosting (XGBoost), and Lasso) for optimizing four machine learning models (Random forest (RF), XGBoost, Logistic regression (LR), and Suppo...

Deep learning-based identification of vertebral fracture and osteoporosis in lateral spine radiographs and DXA vertebral fracture assessment to predict incident fracture.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research
Deep learning (DL) identification of vertebral fractures and osteoporosis in lateral spine radiographs and DXA vertebral fracture assessment (VFA) images may improve fracture risk assessment in older adults. In 26 299 lateral spine radiographs from 9...

Cost-effectiveness of opportunistic osteoporosis screening using chest radiographs with deep learning in Germany.

Aging clinical and experimental research
BACKGROUND: Osteoporosis is often underdiagnosed due to limitations in traditional screening methods, leading to missed early intervention opportunities. AI-driven screening using chest radiographs could improve early detection, reduce fracture risk,...

Feasibility of Bone Mineral Density and Bone Microarchitecture Assessment Using Deep Learning With a Convolutional Neural Network.

Journal of computer assisted tomography
OBJECTIVES: We evaluated the feasibility of using deep learning with a convolutional neural network for predicting bone mineral density (BMD) and bone microarchitecture from conventional computed tomography (CT) images acquired by multivendor scanner...

AI-Enabled Exoskeletal Robotics for Enhancing Mobility, Bone Regeneration, and Functional Rehabilitation in Osteoporosis: A Literature Review.

Studies in health technology and informatics
The care of osteoporosis is being revolutionized by developments in AI-enabled exoskeletal robotics, which can improve mobility and facilitate the recovery from fragility fractures. The literature on current advancements in exoskeletal robotics and t...

Accuracy of Low-Dose Chest CT-Based Artificial Intelligence Models in Osteoporosis Detection: A Systematic Review and Meta-analysis.

Calcified tissue international
The purpose of this study is to systematically review and evaluate the accuracy of low-dose chest CT-based artificial intelligence in osteoporosis screening. A systematic literature search for relevant studies up to 13th December 2024 was performed i...